The high-throughput measurement devices for DNA, RNA, and proteins produce large amount of information-rich data from biological dynamic systems. It is a need to reverse engineering these data to reveal parameters/structure and behavior relationships implicit in the data. Ultimately, complex interactions between its components that make up a system can be better understood. However, issues of reverse engineering in bioinformatics like algorithms use, the number of temporal sample, continuous or discrete type of input data, etc. are discussed but merely in the validity problem. We argue that, since the data available in reality are not so perfect, the result of reverse engineering is impacted by the un-perfect data. If this is true, to know how this impacts the results of the reverse engineering and to what extent is an important issue. We choose the parameter estimation as our task of reverse engineering and develop a novel method to investigate this validity problem. The data we used has a minor deviation from real data in each data point and then we compare the results of reverse engineering with its target parameters. It can be realized that the more error in data will introduce more serious validity problem in reverse engineering. Three artificial systems are used as test bed to demonstrate our approach. The results of the experiments show, a minor deviation in data may introduce large parameter deviation in the parameter solutions. We conclude that we should not ignore the data error in reverse engineering. To have more knowledge of this phenomenon, we further develop an analytical procedure to analyze the dynamic of the systems to see which characteristic will contribute to this impact. The sensitivity test, propagation analysis and impact factor analysis are applied to the systems. Some qualitative rules that describe the relationship between the results of reverse engineering and the dynamics of the system are summarized. All the finding of this exploration research needs more study to confirm its results. Along this line of research, the biological meaning and the possible relationship between robustness and the variation in parameters in reverse engineering is worth to study in the future. The better reverse algorithm to avoid this validity problem is another topic for future work.

No description available.

<p>Användning av mjukvaruverktyg när reverse engineering utförs är ett måste för att få ett användbart resultat enligt Hainaut (2002). Kudrass m fl (1997) visar på brister som kan uppstå när mjukvaruverktyg används. Gemensamt för de forskare som har studerats i detta arbete är att de hävdar att företag saknar kunskap för att utföra reverse engineering med mjukvaruverktyg. Har forskarna rätt i att det saknas kunskap eller har företagen mer kunskap än vad forskarna tror? Då dessa forskare har en klar koppling till tillverkning av mjukvaruverktyg så är eventuellt inte deras påstående opartiska och därför är det intressant att se om deras påstående stämmer med verkligheten. För att undersöka hur mycket kunskap olika företag har om utförande av reverse engineering har intervjuer utförts för att få fram information som sedan jämfördes med forskarnas påstående. Av de företag som deltog i denna undersökning blev resultatet att forskarnas påstående inte kunde styrkas, det finns ingen kunskapsbrist på svenska företag idag inom reverse engineering. Och den naturliga kopplingen mellan mjukvaruverktyg och reverse engineering är mer framtvingad av leverantörer än naturlig för företagen, enligt några av de företag som deltog. Eftersom detta arbete är begränsat i tid så kan inte resultatet som har framkommit ses som att forskarna har rätt eller fel utan bara visa en liten del av vad företagen har för kunskap inom reverse engineering.</p>

<p>More than half of the total costs in ownership of a software system are maintenance costs. Reverse engineering is becoming more important and complex for huge systems, and tools for reverse engineering are necessary for system evaluation.</p><p>The ISO/IEC 9126 standard defines software quality and The VizzAnalyzer</p><p>Framework is a stand-alone tool for analyzing and visualizing large software systems’ structures.</p><p>In this thesis, we describe the design and implementation of plug-ins for the VizzAnalyzer Framework, a system for reverse engineering, extending their features under the standards of software quality. It has proven to be useful in applying the new features into the VizzAnalyzer Framework being the first tool that includes a software</p><p>quality model.</p>

Reverse engineering is broadly defined as the process of analyzing existing products to learn how to create better products in the future. Including reverse engineering as part of the engineering design process can provide a number of benefits, including a more thorough understanding of existing products, lower cost for the redesign of products, and faster times to market. While reverse engineering can be applied to a wide range of domains, this thesis deals with methodologies for extracting technical data from electro-mechanical products for the purpose of recreating them functionally and dimensionally, to an acceptable level of accuracy. An integrated and evolved reverse engineering methodology is presented. This new methodology is built upon previous work, and results from an effort to integrate all previous methods into the simplest and most useful form. Five novel reverse engineering techniques are introduced to solve problems previously unaddressed in the literature: Bounding Pertinent Geometry, Reassembly by Function, Determining Sample Size, Estimating Production Volume and Accounting for Physical Degradation. Throughout the thesis, a running example of the reverse engineering of the Craftsman Auto Hammer is used to illustrate the application of the evolved methodology. / text

published_or_final_version / abstract / toc / Mechanical Engineering / Master / Master of Philosophy

The section for volume, flow and temperature at SP Technical Research Institute of Sweden performs measurements of volume, flow and temperature in liquids. Flow meters are best calibrated in its installation to take sources of error like installation effects and the medium into account. If this can be done without having to place measurement equipment inside the pipe it will mean several practical benefits. Since many years, clamp-on ultrasonic flow meters have been available on the market. But even with today’s improvements they still have a measurement uncertainty in the measurements that is five to ten times too big to make them useful as references for calibration procedures. This thesis focuses on analysis, using reversed engineering, of an existing clamp-on ultrasonic flow meter. The goal of the project is evaluation and further development of the ultrasonic flow meter’s existing computer interface with the purpose of offering the option of using Microsoft Excel and Visual Basic for data acquisition and measurement of the flow rate of liquids.

Gene regulatory network (GRN) consists of a set of genes and regulatory relationships between the genes. As outputs of the GRN, gene expression data contain important information that can be used to reconstruct the GRN to a certain degree. However, the reverse engineer of GRNs from gene expression data is a challenging problem in systems biology. Conventional methods fail in inferring GRNs from gene expression data because of the relative less number of observations compared with the large number of the genes. The inherent noises in the data make the inference accuracy relatively low and the combinatorial explosion nature of the problem makes the inference task extremely difficult. This study aims at reconstructing the GRNs from time-course gene expression data based on GRN models using system identification and parameter estimation methods. The main content consists of three parts: (1) a review of the methods for reverse engineering of GRNs, (2) reverse engineering of GRNs based on linear models and (3) reverse engineering of GRNs based on a nonlinear model, specifically S-systems. In the first part, after the necessary background and challenges of the problem are introduced, various methods for the inference of GRNs are comprehensively reviewed from two aspects: models and inference algorithms. The advantages and disadvantages of each method are discussed. The second part focus on inferring GRNs from time-course gene expression data based on linear models. First, the statistical properties of two sparse penalties, adaptive LASSO and SCAD, with an autoregressive model are studied. It shows that the proposed methods using these two penalties can asymptotically reconstruct the underlying networks. This provides a solid foundation for these methods and their extensions. Second, the integration of multiple datasets should be able to improve the accuracy of the GRN inference. A novel method, Huber group LASSO, is developed to infer GRNs from multiple time-course data, which is also robust to large noises and outliers that the data may contain. An efficient algorithm is also developed and its convergence analysis is provided. The third part can be further divided into two phases: estimating the parameters of S-systems with system structure known and inferring the S-systems without knowing the system structure. Two methods, alternating weighted least squares (AWLS) and auxiliary function guided coordinate descent (AFGCD), have been developed to estimate the parameters of S-systems from time-course data. AWLS takes advantage of the special structure of S-systems and significantly outperforms one existing method, alternating regression (AR). AFGCD uses the auxiliary function and coordinate descent techniques to get the smart and efficient iteration formula and its convergence is theoretically guaranteed. Without knowing the system structure, taking advantage of the special structure of the S-system model, a novel method, pruning separable parameter estimation algorithm (PSPEA) is developed to locally infer the S-systems. PSPEA is then combined with continuous genetic algorithm (CGA) to form a hybrid algorithm which can globally reconstruct the S-systems.

Sequence diagrams are a popular way to visualize dynamic software execution traces. However, they tend to be extremely large, causing significant scalability problems. Not only is it difficult from a technical perspective to build interactive sequence diagram tools that are able to display large traces, it is also difficult for people to understand them. While cognitive support theory exists to help cope with the later problem, no work to date has described how to implement the cognitive support theory in sequence diagram tools. In this thesis, we tackle both the technical and cognitive support problems. First, we use previous research about cognitive support feature requirements to design and engineer an interactive, widget-based sequence diagram visualization. After implementing the visualization, we use benchmarks to test its scalability and ensure that it is efficient enough to be used in realistic applications. Then, we present two novel approaches for reducing the cognitive overhead required to understand large sequence diagrams. The first approach is to compact sequence diagrams using loops found in source code. We present an algorithm that is able to compact diagrams by up to 80%. The second approach is called the trace-focused user interface which uses software reconnaissance to create a degree-of-interest model to help users focus on particular software features and navigate to portions of the sequence diagram that are related to those features. We present a small user study that indicates the viability of the trace-focused user interface. Finally, we present the results of a small survey that indicates that users of the software find the loop compaction and the trace-focused user interface both useful. / Graduate

Thesis (Ph. D.)--University of Hawaii at Manoa, 1994. / Includes bibliographical references (leaves 350-354). / Microfiche. / xx, 354 leaves, bound ill. 29 cm